Generating word embeddings using CBOW and skipgram representations

In Chapter 7, Analyzing Text Data, we already dealt with this topic. In the Word2Vec using gensim recipe, we used the gensim library to build a word2vec model. Now, we will deepen the topic. Word embedding allows the computer to memorize both semantic and syntactic information of words starting from an unknown corpus and constructs a vector space in which the vectors of words are closer if the words occur in the same linguistic contexts, that is, if they are recognized as semantically more similar. Word2vec is a set of templates that are used to produce word embedding.

Getting ready

In this recipe, we will use the gensim library to generate word embeddings...